On modelling response propensity for dwelling unit (DU) level non-response adjustment in the Medical Expenditure Panel Survey (MEPS).

Non-response is a common problem in household sample surveys. The Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ), is a complex national probability sample survey. The survey is designed to produce annual national and regional estimates of health-care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. The MEPS sample is a sub-sample of respondents to the prior year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The MEPS, like most sample surveys, experiences unit, or total, non-response despite intensive efforts to maximize response rates. This paper summarizes research on comparing alternative approaches for modelling response propensity to compensate for dwelling unit (DU), i.e. household level non-response in the MEPS.Non-response in sample surveys is usually compensated for by some form of weighting adjustment to reduce the bias in survey estimates. To compensate for potential bias in survey estimates in the MEPS, two separate non-response adjustments are carried out. The first is an adjustment for DU level non-response at the round one interview to account for non-response among those households subsampled from NHIS for the MEPS. The second non-response adjustment is a person level adjustment to compensate for attrition across the five rounds of data collection. This paper deals only with the DU level non-response adjustment. Currently, the categorical search tree algorithm method, the chi-squared automatic interaction detector (CHAID), is used to model the response probability at the DU level and to create the non-response adjustment cells. In this study, we investigate an alternative approach, i.e. logistic regression to model the response probability. Main effects models and models with interaction terms are both evaluated. We further examine inclusion of the base weights as a covariate in the logistic models. We compare variability of weights of the two alternative response propensity approaches as well as direct use of propensity scores. The logistic regression approaches produce results similar to CHAID; however, using propensity scores from logistic models with interaction terms to form five classification groups for weight adjustment appears to perform best in terms of limiting variability and bias. Published in 2007 by John Wiley & Sons, Ltd.